The present disclosure relates generally to recommendation systems, and more particularly to user-powered recommendation systems.
Recommendation systems are widely used on the Internet. Users rely on recommendation systems, for example, for deciding which news articles to read, which movies to watch, and which digital cameras to purchase. Recommendation systems have become popular for several reasons. Since a vast quantity of information is available on the Internet, a filtering mechanism is needed to obtain information of interest. Furthermore, many recommendations respond to requests for subjective information (for example, “What are good science-fiction movies from the 1960's?”). Therefore, it is important to identify communities of respondents that are likely to provide relevant information to the user. In answer to the above query, for example, recommendations from science-fiction fans are more likely to be relevant than recommendations from respondents with a strong distaste for the genre. Current recommendation systems typically employ collaborative filtering techniques to automatically identify communities of people on the Internet with backgrounds, interests, and tastes similar to those of the user.
If the user associates only with people having similar background, interests, and tastes, however, he may miss opportunities to expand his horizons, to explore new forms of art, food, movies, and literature. Depending on a user's particular needs at a particular instant, the most appropriate community of potential respondents may vary. What are needed are recommendation systems which allow the user to select the community of potential respondents. Recommendation systems which allow the user to select additional criteria and algorithms for outputting recommendations are advantageous.